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首页> 外文期刊>Applied Soft Computing >An artificial neural network model for the effects of chicken manure on ground water
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An artificial neural network model for the effects of chicken manure on ground water

机译：鸡粪对地下水影响的人工神经网络模型

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摘要

In the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms.
机译：在肉鸡业所在地，养鸡场的家禽粪便可能是地下水污染的主要来源，这可能会产生广泛的影响，特别是当养殖场使用附近的地下水作为其淡水供应时。因此，从严格的数学扩散模型或从实验数据评估的角度预测这种污染的程度都具有重要意义。在这项工作中，我们通过人工神经网络研究了鸡粪对地下水的影响的建模。建立了一个人工神经网络模型来预测家禽场地下水井中的大肠菌群总数。使用反向传播算法来训练和测试网络，并使用Levenberg-Marquardt算法进行优化。使用带有神经网络工具箱的MATLAB 7.0环境进行编码。给定相关的输入参数，例如鸡的数量，粪便池管理的类型和井的深度，该模型将井中总大肠菌群的可能数量估计到令人满意的程度。因此，预计在将来对养鸡场造成的地下水污染进行估计将有所帮助。

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